Summary of Key Points
The Deep Economy is a new economic paradigm driven by AI, focusing on utilizing artificial intelligence to uncover "hidden needs" that users are not even aware of (such as anxiety about identity and a desire for belonging). It shifts from the traditional focus on "large scale and low cost" (economies of scale) or "sharing resources across multiple products" (economies of scope) to pursuing "value density." By redefining the logic of value creation through human-machine collaboration, businesses can go from passively responding to consumer demands to actively creating them. While this offers new competitive advantages, it also brings risks and ethical challenges, including misjudging consumer needs, breaching privacy, and manipulating users.
I. The Deep Economy: More Than Just Meeting Your Needs; Understanding What You Don’t Express
The "depth" in the Deep Economy lies in the extent to which it explores consumer needs. We often express explicit desires, such as wanting a phone with a long battery life or delicious food. However, there are also hidden desires, like using our phones to showcase our identity or using food to alleviate loneliness—needs that we are not even aware of.
Needs can be viewed as a spectrum, ranging from superficial to profound. Users move from knowing exactly what they want to having no idea at all, and businesses shift from passively receiving orders to proactively identifying potential needs. Competition also evolves, from competing for established markets to gaining an edge by possessing information that others do not have. For example, Apple doesn’t just sell phones; its ecosystem (AirPods, iCloud) helps users realize they need a seamless connected lifestyle—this is the result of uncovering hidden desires.
II. The Deep Economy vs. Economies of Scale/Scope: Three Approaches to Profit, What Are the Differences?
In the past, businesses primarily relied on two strategies to make money:
- Economies of scale: Higher production volumes reduce costs (e.g., factories mass-producing clothing).
- Economies of scope: Sharing resources across multiple products (e.g., e-commerce platforms selling both clothes and home appliances under one platform, sharing logistics).
The Deep Economy represents a third approach: it generates excess profits by tapping into hidden consumer needs rather than relying on cost reduction. Examples include:
- Amazon’s AWS cloud computing service, which benefits from economies of scale due to global infrastructure;
- Shared logistics networks used by both e-commerce and cloud computing services;
- Personalized recommendations that use algorithms to predict what consumers might buy, even before they consider it themselves.
These three approaches are not mutually exclusive. Successful businesses combine them; for instance, ByteDance uses algorithms to capture user attention (depth) while leveraging scale to reduce content distribution costs.
III. How Do Businesses Operate in the Deep Economy? From Responding to Creating Needs?
Traditional businesses focus on providing what consumers already want, but in the Deep Economy, the goal is to help them discover their unmet needs. There are three main strategies:
1. Narrow positioning: Focusing solely on explicit needs (e.g., producing phones with long battery life).
2. Continuous positioning: Addressing a broader range of needs (e.g., designing phones that not only have good battery life but also excellent photography and gaming capabilities, and creating scenarios where users feel the phone can aid in social interactions).
3. Guided positioning: The most advanced approach, where businesses create needs from scratch (e.g., Apple making its ecosystem indispensable, or Tesla making electric vehicles more than just a mode of transport but a symbol of a technological lifestyle).
To achieve this, businesses need three capabilities:
- Scenario design: Creating environments that trigger hidden desires (e.g., coffee shops with quiet corners).
- Content creation: Using AI to generate personalized content (e.g., video platforms recommending videos you will enjoy).
- Ecosystem collaboration: Connecting with upstream and downstream partners to create a cohesive user experience.
IV. The Risks of Overexploiting the Deep Economy
While the Deep Economy has potential, it also comes with significant risks:
1. Business Risks
- Unrealistic needs: Algorithms may mistake occasional behavior for persistent preferences (e.g., recommending奶茶 based on a single purchase).
- Outdated needs: Trends may change quickly; if businesses fail to adapt, they can miss out (e.g., the shift from luxury goods as status symbols to environmental sustainability).
- Insufficient depth: Claiming to offer personalization might just be a new form of homogenized competition.
2. Ethical Risks
- Privacy breaches: Collecting large amounts of user data for hidden needs can lead to feelings of surveillance.
- Manipulation of irrational desires: Algorithms may push products based on consumer habits, such as shopping addictions, or use tactics like limited editions and flash sales to stimulate impulsive buying.
- Group behavior: Guiding consumers towards irrational trends (e.g., the pursuit of luxury brands) can lead to societal issues.
The fundamental question is whether businesses aim to "serve" or "manipulate" users. For example, while car manufacturers can enhance acceleration for a thrill, it may be unsafe for ordinary drivers to drive at such high speeds.
Conclusion
The Deep Economy is the future trend, but businesses must not lose sight of ethical boundaries. It has the potential to make the economy more efficient and personalized, but they must also ensure they do not treat users as mere data sources. They should create long-term value by truly understanding and meeting their needs. After all, the depth of this new economic model can lead to positive outcomes or negative consequences, depending on how businesses use their power.